RepoMind

Overview

RepoMind leverages Large Language Models to provide intelligent analysis of software repositories, extracting architectural patterns, dependencies, and domain knowledge from codebases.

Core Capabilities

Semantic Code Understanding

• Context-aware analysis beyond syntax parsing
• Identification of design patterns and architectural decisions
• Cross-file relationship mapping

Knowledge Graph Generation

• Automatic construction of codebase knowledge graphs
• Entity extraction (functions, classes, modules)
• Relationship inference between components

Natural Language Querying

• Ask questions about the codebase in plain English
• Get explanations of complex code sections
• Understand the "why" behind implementation choices

Technical Implementation

The system combines multiple techniques:

1. AST Analysis: Traditional parsing for structural understanding
2. Embedding Generation: Semantic embeddings of code segments
3. LLM Integration: GPT-4 and Claude for deep reasoning
4. Vector Search: Efficient retrieval of relevant code sections

Research Applications

This tool serves as a testbed for exploring: - Code comprehension at scale - Automated documentation generation - Legacy system understanding - Cross-language pattern recognition

Example Usage

from repomind import RepositoryAnalyzer

analyzer = RepositoryAnalyzer("path/to/repo")
analyzer.build_index()

# Query the codebase
response = analyzer.query(
    "How does the authentication system work?"
)

# Generate architecture diagram
analyzer.visualize_architecture()

Impact

RepoMind demonstrates how LLMs can augment traditional program analysis, providing insights that would be difficult or impossible to obtain through static analysis alone. This bridges the gap between code-as-text and code-as-knowledge.